2011 on the Processing of Aerial LiDAR Data for Supporting Enhancement, Interpretation and Mapping

8/2/2019 2011 on the Processing of Aerial LiDAR Data for Supporting Enhancement, Interpretation and Mapping

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B. Murgante et al. (Eds.): ICCSA 2011, Part II, LNCS 6783, pp. 392406, 2011.

Springer-Verlag Berlin Heidelberg 2011

On the Processing of Aerial LiDAR Data for Supporting

Enhancement, Interpretation and Mapping of

Archaeological Features

Rosa Lasaponara1 and Nicola Masini2

1 CNR-IMAA (Institute of Methodologies For Environmental Analysis),

Potenza, Italy

[email protected] CNR-IBAM (Institute of Archaeological and Architectural Heritage),

Potenza, Italy

[email protected]

Abstract. Airborne Laser Scanning (ALS) technology, also referred to as

LiDAR (Light Detection and Ranging), represents the most relevant

advancement of Earth Observation (EO) techniques for obtaining high-

precision information about the Earths surface. This includes basic terrain

mapping (Digital terrain model, bathymetry, corridor mapping), vegetation

cover (forest assessment and inventory), coastal and urban areas, etc..

Recent studies examined the possibility of using ALS in archaeologicalinvestigations to identify earthworks, although the ability of ALS measurements

in this context has not yet been studied in detail. It is widely recognized that

there are numerous open issues that must be addressed. The most important of

these limitations are: (i) data processing, (ii) interpretation and (iii) reliable

mapping of archaeological features. In particular, there is a pressing need to

generate very detailed maps of subtle archaeological remains as required for

planning field survey, excavations etc.

Up to now, the visualisation has been approached using hill-shaded LiDAR

DTMs, namely different DTMs are produced by different illuminations from

arbitrary azimuths and elevations using GIS hill-shading techniques. But

numerous limitations characterize this approach, mainly linked to the following

aspects: (i) the use of numerous hill-shaded LiDAR DTMs is time consuming,

(ii) the same features may be replicated from several angles, (iii) the

interpretation is strongly subjective (depending on the interpreter), and (iv) this

implies the impossibility to have reliable maps.

In this paper, these critical issues have been addressed using: 1) slope and

convexity algorithms; 2) Principal Component Analysis (PCA) of hill-shaded

LiDAR DTMs.

The investigations were carried out near Monte Irsi, a significantarchaeological area in the Basilicata Region (Southern Italy) characterized by

complex topographical and morphological features.

Keywords: LiDAR, archaeology, full-waveform, hill shading, PCA, slope,

convexity.


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On the Processing of Aerial LiDAR Data 393

1 Introduction

The advent of LiDAR technologies (Light Detection And Ranging), generally known

as Airborne Laser Scanner (ALS), has completely revolutionized the area of topog-

raphic surveying. ALS is an active remote sensing technology, based on laser pulses,which measures properties of scattered light to find range and/or other information of

a distant target. The range to an object is determined by measuring the time delay

between transmission of a pulse and detection of the reflected signal.

ALS can be mounted onboard on airplane or helicopter. The acquisition system

consists of the following individual components: (i) laser ranging device, (ii) Inertial

Measuring Unit (IMU), (iii) onboard Global Positioning System (GPS) device, (iv)

ground GPS data acquisition at the same time as the LiDAR survey (mandatory for an

accurate georeferecing process, (v) Digital Camera (Optional). While each of these

components are operating independently, integration of all of them allows us to obtainmeasurements with a high level of accuracy.

Currently, two different types of ALS sensor systems are available: (i) conven-

tional scanners (or discrete echo scanners) and (ii) full-waveform (FW) scanners. The

conventional scanners only record some representative signals (generally the first and

last pulse) from the echo waveform, thus losing many other reflections. The full-

waveform (FW) scanners are able to detect the entire echo waveform for each emitted

laser beam, thus offering improved capabilities especially in areas with complex mor-

phology and/or dense vegetation cover.

The LiDAR technology exceeds other methods, such as stereo-photogrammetry orinterferometric SAR, particularly in vegetated areas due to its ability to see through

gaps in canopy forming trees.

ALS provides a detailed digital surface model which can efficiently enable the

identification of archaeological sites and features, which leave traces in relief, but

can not detect buried structures without (micro-) relief.

The high resolution of LiDAR-based DTM allowed us to identify and record small

differences in height on the ground produced by surface and shallow archaeological

remains (the so-called shadow marks) which can not be seen from other data sets,

such as satellite optical images and air photographs.

Nowadays, the majority of archaeological investigations have been carried out us-ing data collected by conventional ALS, for the management of archaeological

monuments [3], for landscape studies [4] and archaeological investigations to depict

microtopographic earthworks in bare ground sites [5] and in forested areas [6,7]

Up to now, the potential of FW LiDAR for archaeological purposes has been as-

sessed in a few studies by Doneus et al [8] who investigated an Iron Age hill fort

covered by dense vegetation and by the authors [9,10] who performed investigations

on two medieval settlements, located on ground hilly places with low dense

vegetation cover.

Despite the high capability of ALS in archaeological investigations, for both con-ventional scanners and full-waveform scanners, there are a number of open issues

that must be addressed. Among these limitations, the most important are: (i) the data

processing chain, (ii) interpretation and (iii) accurate mapping of subtle archaeologi-

cal features. In particular, there is a pressing need to generate very accurate maps of

subtle archaeological remains.


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394 R. Lasaponara and N. Masini

These limitations are particularly relevant for archaeological investigations being

that the structures under investigation are usually characterized by very low reliefwhich exhibit subtle features (i.e. small elevation range compared to the surroundinglandscape). Moreover, the visibility and, therefore, the detectability of these archaeo-

logical features strongly vary according to the direction of the illumination. Forexample, linear features are not visible if they are aligned with the direction of theillumination. To reduce this drawback, some authors suggested the use of numerous

hill-shaded LiDAR DTMs, in order to consider several different illumination anglesfor the interpretation process. Nevertheless, the analysis of diverse combinations ofillumination directions has several limitations, above all (i) it is time consuming, (ii) it

may be inconsistent being linked to a subjective evaluation, (iii) using diverse hill-shaded LiDAR DTMs the same features may be replicated from several angles andthus implying a low accuracy level.

Image analysis may cope with these drawbacks, offering techniques to quantita-tively reduce the redundancy of hill-shaded LiDAR DTMs, and, at the same time,capturing the most significant information.

In this paper, we will present and discuss the data processing approach we adoptedto support the enhancement, interpretation and detailed mapping of subtle featureslinked to archaeological micro-relief.

The proposed approach has been applied to the medieval village of Monte Irsi, lo-cated in Basilicata on a hilly plateau characterized by the significant presence of low

vegetation.The data processing chain includes: 1) slope and convexity extraction from DTM;

2) hill-shaded DTMs and 3) Principal Component Analysis (PCA) of the hill-shadedDTMs. The reconnaissance of archaeological features has been carried out by convex-

ity, whereas hill-shaded DTMs were used to extract them with the highest spatial ac-curacy. Moreover, hill-shaded representations of a LiDAR DTM have been further

elaborated using PCA to effectively reduce the multiple images to a single product forinterpretation and mapping.

This data processing chain enable us to (i) reduce the multiple images to a singleproduct for interpretation and mapping, (ii) to capture all the possible archaeological

features and (iii) to extract them with the highest spatial accuracy. This approachgreatly sharpens the visibility of small-scale and shallow topographic features and

synthesizes the most relevant information in a single product evaluated by using con-vexity.

The text is organized as follows: in Section 2 we describe the study area and theprevious investigations; in Section 3, we focus on data processing issues; in Sections

4 the results are discussed; conclusions follow in section 5.

2 Study Area and Previous Analyses

Monte Irsi is a hilly plateau, near the confluence of the Bradano and Basentello rivers,in the Northeast of Basilicata (Southern Italy). From the geological point of view, it islocalised in the Bradanic Foredeep (or Bradanic Trough), a wide depression locatedbetween the southern Apennines to the west and the Apulian foreland to the east.

From a geodynamic point of view, the origin of Bradanic Trough is to be found in thedeformation sustained from Apulia caused by the elastic bending of the lithosphere.


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The hill of Irsi is a strategic place where it is possible to observe two rivers, Basen-

tello and Bradano, three roads: 1) one followed the Bradano river and it ended at theIonian coast; 2) the second crossed the valleys and the hilly slopes of the LucanianApennines (Northwestern direction); 3) the third road axis connected Monte Irsi to

Venosa, an important town during the Roman age. This strategic location of MonteIrsi favored a long human frequentation, as testified by the archaeological findingsunearthed during the excavation campaigns and field works carried out by Cherry and

Whitehouse [11] and Alaistair Small [12] since 1970. In particular, the archaeologicalrecords put in evidence an important human activity in the Late Iron age (6

th-4

thcen-

tury B.C.), when probably the hill was settled [12], in the late Hellenistic period and

in the imperial roman age [13]. The latter is testified by a villa unearthed by Small,near a Church (of S. Maria dIrsi) which is the only preserved architectural evidence.

As regards to the historical studies, the tradition supposes a Byzantine settlement

which was destroyed by the Saracens in 988. The first documentary source availabledates back to the 12th century. It is a papal bull, issued in 1123, which refers a settle-

ment, named castrum Ursum belonging to the diocese of Montepeloso (today Irsina).In 1133, Castrum Ursum (named also Yrsum in other documents), was given to theBenedectine Priorate of S. Maria dello Juso [14].

Fig. 1. Location of Yrsum

The first information on the demographic consistency dates back to the 13th

century. The census of the year 1277 did record 114 families, corresponding to apopulation ranging from 700-to 900 inhabitants, quite significant compared to thesurrounding villages. In that time, Yrsum was in decline, as testified by the Prior of S.

Maria dello Juso, who described the state of poverty of the people living in the vil-lage in a letter to the King dated 1272. The only information on the medieval urbanfabric dates back to 1288. A deed of sale of a house in Yrsum refers to the presence

of a church, two houses and a platea puplica (public square) [15]. Unfortunately, no

documented information about the presence of a monastery is available. It may be inthe structures of the baroque church, located at 200 m on the southeast side of the ur-

ban perimeter.At the end of the 13th century, the decline of Yrsum was increasing so that, in

1294, it obtained exemption from the tax payment by the King [16].The tax exemp-


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tion did not free the village from poverty, as testified by the 1320 census which

recorded a decrease in population [15].Between 1370 and 1376, the people of Montepeloso with the moral support of the

Duke of Andria, Francesco del Balzo, first looted the monastery, then they devastated

all its properties, among which the village ofYrsum [14]. Such devastations markedthe end ofYrsum, which is thought to be abandoned not long after.

Starting from the archaeological record and the historical data described in the pre-

vious section, integrated investigations based on the use of remote sensing have beenperformed, since 2005.

By means of the integration of VHR satellite imagery and field survey it was possible

to identify the main anthropic witnesses referable to the medieval age. In particular, theditch of a castle, some crop marks and microrelief related to the urban fabric of the me-dieval village [2, 15, 17]. The optical dataset showed significant limits in detecting all

the archaeological microrelief, thus preventing the reconstruction of the urban shape. Toovercome such limits and to survey the underwood, a LiDAR survey has been carriedout on September 2008. The DTM derived by the point clouds taken from a full-

waveform laser scanner mounted on an helicopter allowed us to detect a greater numberof archaeological microrelief very useful to us for studying the urban morphology. Inparticular, three urban sectors (see B, C and D in figure 2) have been identified [9],

among which one interested by a landslide [10] which could be probably one of thefactors which caused the abandonment of the medieval village. The analysis of DTM

under vegetation and wood revealed traces of small potential ancient field divisionswhich suggest intensive farming in this area, likely related to vineyard, vegetable gar-

dens and fruit trees which supplied the people living in Yrsum [18].

Fig. 2. 3d DTM of the medieval village of Yrsum in Monte Irsi


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The above described information provided by the remotely sensed data (satellite

imagery and DTM LiDAR) greatly improved the knowledge of the medieval urban

fabric of Yrsum. Nevertheless, in order to use such data for future archaeological ex-

cavation campaigns, a map of microrelief with the location of possible buried or shal-

low walls needs to be done.To this aim, a post processing approach by using shaded DTMs and other topog-

raphical modeling has been adopted, as shown in section 3.3.

Fig. 3. Subset of Yrsum: detail of microrelief

3 Airborne Laser Scanning in Archaeology: Data Processing

Methodology

3.1 Data Filtering

The identification of archaeological features, from earthworks to surface structures in

both bare and densely vegetated areas, needs a DTM with a high accuracy. To this aim,

it is crucial to carry out the classification of terrain and off terrain points by applying

adequate procedures. Several filtering methods are available and used for this task.

In the current study, both Digital Surface Model (DSMs) and Digital Terrain

Model (DTMs) are obtained from the classification performed using a strategy based

on a set of filtrations of the filtrate. The workflow can be summarized as follows: i)Low point Classification; ii) Isolated points Classification; iii) Air points; iv) Ground

Classification; v) Classification of points below surface; vi) Classification of points

by class; vii) Classification of points by height from ground for different heights.


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The data classification process starts by including all of the point cloud into a

single class, called the default class, which becomes the reference for the next

processing. The elimination of outliers is performed through classification of : (i)

"low points", (ii) isolated points, and (iii) air points. The first finds single points or

groups of points with an height lower than 0.5 m compared to the other points withina ray of 5 m. The second routine identifies isolated points such as points present in the

air (for example birds, etc..). The third one finds points present in the air not classified

as isolated points.

The next processing step is based on the Axelsson TIN model [19] in an attempt to

define a "ground" surface. To accept or reject points as being representative of the

"ground" is necessary to define some geometric threshold values which prescribe pos-

sible deviations from the average topographic surface. For example, the maximum

building size of the largest buildings. The algorithm looks for the so-called "key

points", i.e. the lowest point that will define a first ground surface made up of very

large triangles. A triangle of the primary mesh is progressively densified by adding a

new vertex to a point inside it.

The Classification of points below surface allows the identification of points un-

der the surface level, such as wells or similar. Such classification was performed set-

ting the standard deviation value at 8 (with 0.01 m tolerance value).

The next two classifications (vi and vii) identify and classify points according to a

given class or heights, respectively. All points left into the default class are now con-

sidered as vegetation. Finally, using Classification of points by height from ground

for different heights three classes are considered: low (< 0.25), medium (0.25 to 2 m)and height ( > 2m). A further classification enables the discriminations of cars, walls,

buildings, vegetation types, etc.

The DTM was created using a commercial software TerraModeler on the basis of

the classification of terrain and off terrain objects performed using the whole process-

ing chain from (i) to (vii) step.

3.2 Post Processing Approach

3.2.1 Topographical ModellingIn order to extract and classify different geomorphological features from DTMs to-

pographical modelling has been applied. There are several ways to model the topog-

raphical surface among which the most employed are the slope and the convexity (

profile, plan, longitudinal and cross section convexity).

The slope is a different way to measure elevation changes. It is the rate of rise or fall

of elevation against horizontal distance, this measures how a surface is inclined. Com-

putation of slope is also used for LiDAR point classification that evaluates sudden

changes in the terrain surface [20]. Convexity represents the first derivative of slope.

For archaeological applications, both slope and convexity maps could be used in

order to better interpret micro-relief referable to buried or shallow structures or

earthworks of cultural interest. Both slope and convexity can facilitate the extraction

of the geometrical patterns referable to buried architecture elements or settlements. To

explain the potential of using slope and convexity to identify micro-relief, it is neces-

sary to resume the diverse phenomena which generates the microrelief. They are


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generally caused by structures collapsed and generally hidden by building material

and terrain which are exposed to erosion rate varying according to the pendency

(slope) and landscape characteristics. The results are generally alternations of convex-

ity and concavity features. Convexity patterns are caused by the presence of buried

walls and/or foundations, whereas the concavity patterns could reveal the presence offloor and/or road paving (see figure 3). For our study case, slope and convexity rou-

tines of ENVI have been used.

3.2.2 DTM Shading

In order to emphasize archaeological features with particular reference to micro-relief

a further crucial step is given by shading procedures. Several routines embedded in

commercial software allow different solutions, such as the visualization of the eleva-

tions by using colour graduations and the slope of the terrain, in order to identify the

portions of the terrain that are relatively flat versus those that are relatively steep.For the visualization of elevations it is useful to enable Hill Shading option to view

elevation data as shaded relief. With this option shadows are generated using the

loaded elevation. To do it, it is necessary to light the DTM by an hypothetical light

source. The selection of the direction parameters (zenith angle z and azimuth angle )depends on the difference in height and orientation of the micro-relief of possible ar-

chaeological interest. Single shading is not the most effective method to visualize and

detect micro-relief. If features and/or objects are parallel to the azimuth angle, will

not rise a shade. As a result, it would not be possible to distinguish them.

The problem could be solved by observing and comparing DTM scenes shaded byusing different angles of lighting.

In addition the different shaded DTMs could processed by using the Principal

Components Analysis (PCA) [21].

3.2.3 Principal Component Analysis

The PCA is a linear transformation which decorrelates multivariate data by translating

and/ or rotating the axes of the original feature space, so that the data can be represented

without correlation in a new component space. In order to do this, the process firstly

computes the covariance matrix (S) among all the shaded DTMs, then eigenvalues andeigenvectors of S are calculated in order to obtain the new feature components.

( )( )==

=

1 1 22,,21,,

1

2,1cov

i j kkjiSBKKJISBnm

kk

(1)

where k1, k2 are two input shaded DTM (SDTM), SB i,j, is the digital number

(DN) value of the SDTM in row i and column j, n number of row, m number of col-

umns, mean of all pixel SB values in the subscripted input SDTM.The percent of total dataset variance explained by each component is obtained by

formula 2.


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=

=

1

*100%

ii

ii

(2)

where i are eigenvalues of S.Finally, a series of new image layers (called eigen-channels or components) are

computed (formula 3) by multiplying, for each pixel, the eigenvector of S for the

original value of a given pixel in the input shaded DTMs.

=

=1

,

i

ikki uPP

(3)

where Pi indicates a SDTM in component i, u k,i eigenvector element for compo-

nent i in input SDTM k, Pk DN.

for SDTM k, number of input SDTM.A loading, or correlation R, of each component i with each input shaded DTMs k

can be calculated by using formula 4.

( ) ( )21

2

1

, var, kik iuiRk = (4)

where var k is the variance of input shaded DTMs k (obtained by reading the kth

diagonal of the covariance matrix).The PCA transforms the input shaded DTMs in new components that should be

able to make the identification of distinct features and surface types easier. The major

portion of the variance is associated with homogeneous areas, whereas localized sur-face anomalies will be enhanced in later components, which contain less of the totaldataset variance. This is the reason why they may represent information variance for

a small area or essentially noise and, in this case, it must be disregarded. Some prob-lems can arise from the fact that eigenvectors can not have general and universal

meaning since they are extracted from the series.

4 Discussion of Results

For Irsi case study, the first step has been the computation of slope and profile con-vexity. The maps obtained (fig. 4) put in evidence microrelief characterized by a sig-nificant elevation change, thus disregarding the subtle microrelief.

Anyway, the maps obtained allow us to extract and analyze both the geomor-

phological (we refer to a landslide which affects the south-eastern slope of the hill)and archaeological features: the latter, from the urban morphology point of view.

In particular, we observe in a more emphasized way respect to the DTM (fig. 4),

two different patterns of urban fabric: i) one, close to the castle, is characterized by amore regular and square grid; ii) the second pattern is given by curvilinear featuresdue to the fact that the settlement adapted to the morphology of the hill.

Such patterns are likely referable to two different historical building phases.Figure 5 shows on the left the convexity map, in the middle the slope map of a sub-

set and its interpretation, on the right: linear segments likely referable to the axis of

buried structures are plotted. Such marks have been assumed as reference for the in-terpretation of the maps obtained by shading procedure.


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Fig. 4. On the left: map derived from the computation of the slope; on the right: map obtained

by computing the profile convexity. Both of them put clearly in evidence the microrelief

referable to the layout of buried buildings of Irsi medieval village.

Fig. 5. On the left: the convexity map; in the middle: slope map; on the right: the interpretationof the two maps (superimposed on the slope map)

Fig. 6. On the left: shaded DTM lighted by light source from East, with z=60 and =0; in themiddle: the same DTM with the interpretation; on the right: the interpretations performed on

the convexity map (see figure 5, right) superimposed on to the image of middle


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The second step consisted just of the shading procedures (following section 3.2.2)

of the DTM. The latter has been lighted by four hypothetical light sources with the

following values of zenith (z) and azimuth angles (): 1) z=60; =0; 2) z=60;=90; 3) z=60; =180; 4) z=60; =270.

The comparative observation of the shaded DTMs put clearly in evidence howdramatically changes the visibility of microrelief, varying the light source direction.

Moreover, the visual interpretation of the single shaded DTM (fig. 6-9) highlightsseveral differences in the visualization of microrelief respect to the interpretation per-formed on the slope map. In particular, the marks surveyed using the shaded DTMs fit

the marks detected from the slope and convexity maps according to the followingpercentage: 55%, 48%, 41%, 44%.

Then, the PCA has been calculated, in order to quantitatively reduce the redun-

dancy of hill-shaded LiDAR DTMs and capture the most significant information.

Fig. 7. On the left: shaded DTM lighted by light sources from East, with z=60 and =90; inthe middle: the same DTM with the interpretation; on the right: the interpretations performed

on the convexity map (see figure 5, right) superimposed on to the image of middle

The interpretation of the four components of the PCA allowed us to increase the

number of marks detected compared to those obtained from the single shaded DTM.Moreover, the marks surveyed fit the marks detected from the slope and convexitymaps with higher percentages compared to the single shaded DTMs. In particular, thebest results were obtained from PC3 and PC4, where the rate of superimposition rang-

ing between 65% and 70 %.


on the convexity map (see figure 5, right) superimposed on to the image of middle


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on the convexity map superimposed on to the image of middle

The results obtained on the test area of figures 5-12 pointed out the effectiveness of

slope and convexity maps, as well as the usefulness of PC3 and PC4 obtained from

shaded DTMs to help the mapping of the archaeological features of Yrsum (see

figure 13).

Fig. 10. On the left: PC1; middle: interpretation (with yellow segments), on the right: blue line

interpretation from convexity map superimposed on to the yellow line related to PC1

interpretation.

Fig. 11. On the left: PC3; middle: interpretation (with green segments), on the right: blue line

interpretation from convexity map superimposed on to the green line related to PC3

interpretation.


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Fig. 12. On the left: PC4; middle: interpretation (with red segments), on the right: blue line

interpretation from convexity map superimposed on to the red line related to PC4 interpretation

Fig. 13. Map of Yrsum: mapping of archaeological features with contour lines

5 Conclusions

In this paper, we focus on the approach we adopted to support the data processing ofaerial LiDAR survey for archaeological investigations. The processing chain has been

devised in order to facilitate the interpretation and mapping of micro-relief linked to

archaeological features.

Analyses were carried out for the medieval village of Monte Irsi, located on a hilly

plateau in the Basilicata Region.


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The procedure included the extraction and interpretation of slope and convexity

maps, to extract the microrelief using an automatic approach, which provides the most

likely pattern features. The advantage of obtaining automatic and objective archaeo-

logical features is coupled with a loss of spatial accuracy due to the computation. To

reduce this effect we used the hill-shading of DTMs. Shadow and saturation of thehill-shading technique are reduced via PCA.

In order to assess the most significant image in term of information content, we

carried out a comparison of the all DTMs and PC with the slope and convexity maps

over a subset. This comparison pointed out that PC3 and PC4 seem to provide the

most satisfactory results, being that features recognized from PC3 and PC4 fit well

with marks obtained from convexity. Finally, convexity along with PC3 and PC4

were assumed as a basis for mapping archaeological features for the whole study area.

This approach greatly sharpens the visibility of small-scale and shallow topog-

raphic features and synthesize the most relevant information in a single productevaluated by using convexity.

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2011 on the Processing of Aerial LiDAR Data for Supporting Enhancement, Interpretation and Mapping